Writing study: One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype
← ICLR 2026 oral writing corpus
Guanjun Wang, Binwu Wang, Jiaming Ma, Zhengyang Zhou, Pengkun Wang, Xu Wang, Yang Wang · Official ICLR 2026 oral record
Figure, table and section ledger
Editorial contribution type: Learning & optimization algorithms (primary) · Architecture & representation design.
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 38 physical pages
- Before references / appendix
- ≈ 10.43 page-equivalents
- Main figures / tables
- 6 / 5
- Attached appendix figures / tables
- 10 / 19
- Attached appendix span
- ≈ 21.87 page-equivalents
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1 INTRODUCTION | 1 | ≈ 1.06 pp. | 2.79% |
| 2 RELATED WORK | 2 | ≈ 0.84 pp. | 2.21% |
| 3 PRELIMINARIES | 3 | ≈ 0.16 pp. | 0.42% |
| 4 METHODOLOGY | 3 | ≈ 3.21 pp. | 8.45% |
| 5 EXPERIMENTS | 6 | ≈ 3.94 pp. | 10.37% |
| 6 CONCLUSION | 10 | ≈ 0.32 pp. | 0.84% |
| ETHICS STATEMENT | 11 | ≈ 0.11 pp. | 0.29% |
| REPRODUCIBILITY STATEMENT | 11 | ≈ 0.21 pp. | 0.55% |
| REFERENCES | 11 | ≈ 5.71 pp. | 15.03% |
| Appendix | 17 | ≈ 0.97 pp. | 2.55% |
| A USE OF LLM | 18 | ≈ 0.09 pp. | 0.24% |
| B RELATED WORK | 18 | ≈ 1.38 pp. | 3.63% |
| C METHOD | 19 | ≈ 0.21 pp. | 0.55% |
| D THEORY PROOF | 19 | ≈ 5.08 pp. | 13.37% |
| E ALGORITHM PSEUDOCODE | 24 | ≈ 0.72 pp. | 1.89% |
| F DEFINITION OF GRAPH CLASS IMBALANCE AND TOPOLOGICAL | 25 | ≈ 0.81 pp. | 2.13% |
| G DATASET | 26 | ≈ 4.07 pp. | 10.71% |
| H EXPERIMENT SETTING | 30 | ≈ 1.04 pp. | 2.74% |
| I ADDITIONAL EXPERIMENTAL RESULTS | 31 | ≈ 7.03 pp. | 18.5% |
| J LIMITATION DISCUSSION AND FUTURE WORK | 38 | ≈ 0.46 pp. | 1.21% |
All detected figure and table locations
Main text
Attached appendix
- Table 6 · p. 24
- Table 7 · p. 25
- Table 8 · p. 26
- Figure 7 · p. 27
- Table 9 · p. 28
- Table 10 · p. 29
- Table 11 · p. 30
- Table 12 · p. 30
- Table 13 · p. 30
- Table 14 · p. 32
- Table 15 · p. 32
- Table 16 · p. 33
- Table 17 · p. 33
- Table 18 · p. 34
- Table 19 · p. 34
- Table 20 · p. 34
- Table 21 · p. 34
- Figure 8 · p. 35
- Figure 9 · p. 35
- Figure 10 · p. 35
- Figure 11 · p. 36
- Figure 12 · p. 36
- Figure 13 · p. 37
- Figure 14 · p. 37
- Table 22 · p. 37
- Table 23 · p. 37
- Table 24 · p. 38
- Figure 15 · p. 38
- Figure 16 · p. 38
Argument and organization
- Introduction and preliminaries
Distinguish class-frequency imbalance from graph-size imbalance before proposing a shared remedy.
- Method and experiments
Build topology encoding, perturbation and balanced prototypes, then evaluate isolated and intertwined imbalance.
- Appendices A–J
Separate related work, derivations, construction protocols, extended tests and limitations.
Problem, claim and contribution
Two clearly named imbalance axes create a testable unification claim; graph-level classification is distinguished from node- and edge-level tasks.
Sections 1–3; Appendix B and F. · Main paper
Argument and section order
Main experiments separate class, topology, intertwined imbalance, ablations and 3D conformations. This organization is more informative than one aggregate leaderboard.
Sections 5.1–5.8, pages 7–10. · Main paper
Figures, captions and visual explanation
Figure 1 expands perception and balancing beneath the pipeline, a useful overview-to-detail pattern. Dense arrows, textured backgrounds and small mathematical labels reduce legibility at normal page size.
Figure 1, page 3. · Main paper
Tables, comparisons and evidence
Backbone-with/without pairs make the claimed plug-in contribution directly inspectable. The AirGraph bubble plot places accuracy, time and memory together, whereas radial bars make exact cross-dataset comparisons harder.
Table 4 and Figures 2–4, page 9. · Main paper
Appendix and reproducibility
Dataset construction tables preserve different binary, multiclass and topology splits. AirGraph's forecasting description would benefit from an explicit chronological partition and leakage protocol.
Appendices G.1.2–G.1.5, pages 26–30. · Main paper
Clarity and writing conventions
Theory should separate a variational upper bound and local Taylor approximation from exact equivalence. Bias placement after TopK and the activation-count update deserve implementation-level clarification before reuse.
Equations 20–35 and Algorithm 1, pages 20–25. · Main paper
What to reuse—and what not to infer
Transferable lesson: Organize a unification paper around independent failure axes, then test each axis and their intersection with clearly paired controls.
Boundary: Balanced prototype use alone does not establish class fairness; the theoretical reduction and discrete routing update are not independently certified. Low-imbalance results include exceptions to broad superiority wording.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-09-19
- Reading status
- Full text + selected visuals inspected
- Main text
- Pages 1–11: Sections 1–6, ethics and reproducibility; references 11–16.
- Appendix / supplement
- Pages 17–38: contents and Appendices A–J, including proofs, pseudocode, splits, all supplementary tables and limitations.
- Visually inspected PDF pages
- 3, 9
- ICLR 2026 final proceedings · 38 pages